NLM DIR Seminar Schedule
UPCOMING SEMINARS
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Jan. 22, 2026 Mario Flores
AI Pipeline for Characterization of the Tumor Microenvironment -
Jan. 27, 2026 Zhaohui Liang
Heterogeneous Graph Re-ranking for CLIP-based Medical Cross-modal Retrieval -
Jan. 29, 2026 Mehdi Bagheri Hamaneh
FastSpel: A simple peptide spectrum predictor that achieves deep learning-level performance at a fraction of the computational cost -
Feb. 3, 2026 Matthew Diller
TBD -
Feb. 5, 2026 Lana Yeganova
From Algorithms to Insights: Bridging AI and Topic Discovery for Large-Scale Biomedical Literature Analysis.
RECENT SEMINARS
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Jan. 22, 2026 Mario Flores
AI Pipeline for Characterization of the Tumor Microenvironment -
Jan. 20, 2026 Anastasia Gulyaeva
Diversity and evolution of the ribovirus class Stelpaviricetes -
Jan. 8, 2026 Won Gyu Kim
LitSense 2.0: AI-powered biomedical information retrieval with sentence and passage level knowledge discovery -
Dec. 16, 2025 Sarvesh Soni
ArchEHR-QA: A Dataset and Shared Task for Grounded Question Answering from Electronic Health Records -
Dec. 2, 2025 Qingqing Zhu
CT-Bench & CARE-CT: Building Reliable Multimodal AI for Lesion Analysis in Computed Tomography
Scheduled Seminars on Dec. 2, 2025
In-person: Building 38A/B2N14 NCBI Library or Meeting Link
Contact NLMDIRSeminarScheduling@mail.nih.gov with questions about this seminar.
Abstract:
Advances in multimodal AI have opened new opportunities for automated lesion analysis in computed tomography (CT), yet progress is limited by the lack of richly annotated datasets and robust evaluation benchmarks. In this seminar, I will present CT-Bench, a large-scale, clinically grounded CT dataset containing 20,335 lesions with bounding boxes, radiologist-derived descriptions, and size measurements, along with a comprehensive QA benchmark covering seven key lesion analysis tasks such as localization, image retrieval, description generation, and attribute classification. I will also introduce CARE-CT, a consistency-aware reflective agent designed to perform multi-step CT reasoning with enhanced reliability. CARE-CT integrates domain-specific tools with consistency scoring and adaptive reflection to identify and correct reasoning errors. Together, CT-Bench and CARE-CT provide a strong foundation for training, evaluating, and improving multimodal AI systems for real-world CT interpretation.